
We visualize the embedding space using the Barnes-Hut t-Distributed stochastic neighbor embedding (t-SNE) technique [1]}. Fig. REF  shows the t-SNE plots when the combination of LoOp and triplet loss is used for training the Cars196 dataset. It can be seen that with increasing epochs, the clusters become more distinct. Further, as training progresses, the synthetic samples (redred) lie within the area occupied by the original samples (blueblue).
